Welcome

Summary

About this Document

This document is an interactive dashboard viewable from most modern internet browsers. The dashboard is a validation and diagnostics tool for Activity Based Models. Users can compare model performance against a household survey as part of a validation exercise or compare two model runs for sensitivity testing. All of the data, charts, and maps viewable in this dashboard are embedded directly into the HTML file. An internet connection is necessary for the best user experience, but is not required.

Users may navigate to different areas of the dashboard using the navigation bar at the top of the page, and may interact directly with most tables, charts, and maps.

This document is best viewed using the most recent versions of the following web browsers:

Note: Mozilla Firefox does not correctly render the images in this HTML file.

Summary

Modeling Region

Overview

Base Highlights

Survey

Base Population

9,083,501

Base Households

3,621,569

Base Tours

13,477,615

Base Trips

36,754,386

Base Stops

11,562,395

Base VMT

195,468,109

Build Highlights

ASim

Build Population

9,387,861

Build Households

3,621,569

Build Tours

12,858,532

Build Trips

34,985,564

Build Stops

9,268,500

Build VMT

146,696,812

Chart Column 1

Person Type Distribution

Household Size Distribution

Base Highlights2

Survey

Tours per Person

Trips per Person

Stops per Person

Trips per Household

Build Highlights2

ASim

Tours per Person

Trips per Person

Stops per Person

Trips per Household

Long Term Models

Chart Column 1

Auto Ownership

Percentage Working From Home

Chart Column 2

Mandatory TLFD

Flows & Tour Lengths

Chart Column 1

County - County Flow of Workers
2011 - 2015 ACS Commuting Flows
X Cook DeKalb DuPage Grundy Kane Kendall Lake..IL McHenry Will Lake..IN LaPorte Porter Total order
Cook 1,889,132 811 100,409 1,289 11,614 295 35,742 2,326 38,479 13,103 0 2,516 2,095,715 1
DeKalb 755 41,941 905 0 3,869 304 0 0 245 0 0 69 48,088 2
DuPage 99,437 438 273,134 62 15,410 4,300 7,748 190 11,932 358 0 0 413,010 3
Grundy 955 0 138 9,761 0 1,521 0 0 6,207 0 0 0 18,582 4
Kane 41,236 1,440 37,145 224 136,468 3,048 7,535 7,518 1,694 0 0 0 236,309 5
Kendall 7,121 495 19,430 460 9,638 34,142 69 69 16,046 0 0 0 87,469 6
Lake, IL 62,031 97 5,430 0 2,538 0 247,937 2,386 95 0 0 0 320,513 7
McHenry 14,503 95 2,234 0 3,973 0 7,353 46,186 94 0 0 0 74,437 8
Porter 7,367 0 403 0 187 0 0 0 268 14,060 3,244 35,128 60,657 9
Lake, IN 40,109 0 1,147 0 0 0 175 0 6,543 91,511 644 6,339 146,467 10
Will 56,744 62 48,872 2,899 6,232 4,278 536 61 167,087 123 0 0 286,894 11
LaPorte 1,470 0 0 0 0 0 0 0 0 1,216 19,421 4,502 26,609 12
Total 2,220,861 45,378 489,246 14,695 189,928 47,887 307,094 58,736 248,689 120,371 23,309 48,554 3,814,750 13

Average Mandatory Tour Lengths
Survey
Home District Work University School
Cook 11.38 9.84 3.69
DeKalb 10.61 5.12 6.22
DuPage 13.48 13.21 3.72
Grundy 17.41 24.31 4.49
Kane 14.98 23.96 5.60
Kendall 15.78 25.07 4.72
Lake, IL 14.41 19.51 4.85
Lake, IN 17.65 16.93 5.53
LaPorte 17.37 19.63 6.84
McHenry 16.83 16.33 4.77
Porter 17.43 15.67 4.56
Will 17.04 22.14 4.97
Total 13.11 12.40 4.24

Chart Column 1

County - County Flow of Workers
ASim
X Boone Cook DeKalb DuPage Grundy Kane Kankakee Kendall Kenosha Lake..IL Lake..IN LaPorte Lasalle McHenry Ogle Porter Racine Walworth Will Winnebago Total
Boone 9,507 1,056 0 754 0 1,056 0 151 0 302 0 0 0 1,811 0 0 0 453 0 11,468 26,558
Cook 151 2,016,610 151 152,559 0 22,333 1,509 905 151 65,037 41,346 302 0 6,640 0 1,207 0 0 44,213 453 2,353,567
DeKalb 302 3,622 19,768 5,131 0 7,092 0 1,056 0 0 0 0 453 1,509 453 0 0 0 1,056 2,263 42,704
DuPage 151 135,960 0 252,604 453 32,443 0 2,113 0 6,036 302 0 0 453 0 0 0 0 17,806 151 448,471
Grundy 0 2,414 151 1,509 7,394 1,358 905 302 0 0 0 0 1,056 0 0 0 0 0 9,658 0 24,747
Kane 453 47,382 1,358 72,582 453 110,760 0 6,941 0 5,432 0 0 302 9,205 151 0 0 151 6,640 905 262,715
Kankakee 0 6,187 0 604 151 151 31,538 0 0 0 1,962 0 0 0 0 151 0 0 6,338 0 47,080
Kendall 151 4,678 604 16,901 302 14,184 0 9,658 0 151 151 0 453 151 151 0 0 0 11,921 151 59,605
Kenosha 0 6,640 0 151 0 302 0 0 35,763 15,844 0 0 0 3,320 0 0 9,959 1,509 0 0 73,488
Lake, IL 151 86,616 0 10,261 0 3,320 0 0 10,865 170,666 0 0 0 17,052 0 0 3,772 754 302 0 303,759
Lake, IN 0 62,321 0 1,962 0 0 453 0 0 0 114,230 905 0 0 0 11,167 0 0 5,281 0 196,319
LaPorte 0 1,509 0 0 0 0 0 0 0 0 3,772 29,123 0 0 0 8,601 0 0 151 0 43,157
Lasalle 0 1,811 1,358 1,207 1,207 1,660 302 1,358 0 0 0 0 34,254 0 151 0 0 0 1,962 151 45,421
Lee 0 151 151 0 0 0 0 0 0 0 0 0 151 0 0 0 0 0 0 302 754
McHenry 453 30,180 754 7,545 0 20,673 0 0 1,660 27,464 0 0 0 47,382 0 0 302 2,565 151 1,660 140,788
Ogle 302 0 905 754 0 302 0 0 0 151 0 0 0 0 4,376 0 0 0 302 3,471 10,563
Porter 0 9,356 0 453 0 0 151 0 0 0 19,768 4,527 0 0 0 34,405 0 0 604 0 69,263
Racine 0 2,867 0 0 0 0 0 0 15,241 6,338 0 0 0 1,962 0 0 57,040 1,811 0 302 85,560
Walworth 1,056 754 0 302 0 604 0 0 1,811 3,169 0 0 0 4,225 0 0 3,622 26,709 0 1,660 43,912
Will 0 85,409 302 74,242 1,660 15,543 2,565 4,980 0 453 5,131 151 604 151 0 302 151 0 149,239 0 340,880
Winnebago 5,131 1,207 604 302 0 302 0 0 0 302 0 0 0 1,509 151 0 0 453 0 106,836 116,796
Total 17,806 2,506,729 26,105 599,822 11,619 232,082 37,423 27,464 65,490 301,345 186,662 35,009 37,272 95,368 5,432 55,833 74,846 34,405 255,622 129,773 4,736,107

Average Mandatory Tour Lengths
ASim
Home District Work University School
Boone 17.36 3.27 6.68
Cook 10.06 1.30 3.83
DeKalb 22.30 3.53 10.21
DuPage 12.67 1.74 5.32
Grundy 26.38 5.08 12.52
Kane 15.71 2.33 5.38
Kankakee 19.67 3.80 10.17
Kendall 20.40 3.84 7.15
Kenosha 16.99 3.00 5.82
Lake, IL 16.07 2.16 5.97
Lake, IN 16.48 3.31 6.22
LaPorte 16.48 4.66 5.54
Lasalle 17.71 3.26 7.30
Lee 44.08 NA 11.17
McHenry 19.84 2.68 7.57
Ogle 21.31 NA 8.14
Porter 18.27 4.03 7.05
Racine 14.49 3.55 4.40
Walworth 19.43 3.37 10.30
Will 17.56 2.96 6.16
Winnebago 10.57 3.35 5.24
Total 13.13 1.94 5.06

Employment vs Workers

Chart Column 2

By County

By TAZ

Low Income

Medium Income

High Income

Very High Income

Total

Zero Auto Households

Summary

Zero Auto Households Census vs Model

Tour Summaries

Chart Column 1

Daily Activity Pattern

Percentage of Households with a Joint Tour

Mandatory Tour Frequency

Chart Column 1

Total Tour Rate (only active Persons)

Persons by Individual Non-Mandatory Tours

Joint Tours

Chart Column 1

Joint Tour Frequency

Joint Tour Composition

Chart Column 1

Joint Tours By Number of Household Members

Joint Tours by Household Size

Party Size Distribution by Joint Tour Composition

Destination

Chart Column 1

Non-Mandatory Tour Length Distribution

Average Non-Mandatory Tour Lengths (Miles)

Purpose Survey ASim
Escorting 5.68 3.97
Indi-Maintenance 8.45 6.10
Indi-Discretionary 7.45 6.81
Joint-Maintenance 8.34 6.61
Joint-Discretionary 8.59 7.10
At-Work 5.46 2.66
Total 7.43 5.52

TOD

Chart Column 1

Tour Departure-Arrival Profile

Tour Aggregate Departure-Arrival Profile

Tour Mode

Chart Column 1

Tour Mode Choice


Tour Mode Choice

Results of Tour Mode Choice Models, which selects a primary mode for each tour.

Distribution of tours by tour mode and the ratio of autos to drivers in the household.

Chart Column 2

Chart Column 3

Stop Frequency

Chart Column 1

Stop Frequency - Directional

Chart Column 1

Stop Frequency - Total

Stop Purpose by Tour Purpose

Location

Chart Column 1

Stop Location - Out of Direction Distance

Chart Column 1

Average Out of Direction Distance (Miles)

_______________________________________________________
Tour_Purpose Survey ASim
Work 5.74 3.23
University 4.43 4.34
School 6.24 4.26
Escorting 5.55 3.57
Indi-Maintenance 5.50 3.47
Indi-Discretionary 6.06 3.47
Joint-Maintenance 4.77 3.31
Joint-Discretionary 6.45 3.47
At-Work 3.77 2.00
Total 5.83 3.44

TOD

Chart Column 1

Stop & Trip Departure

Aggregate Stop & Trip Departure

Trip Mode

Chart Column 1

Trip Mode Choice

The results of the Trip Mode Choice Model, which predicts the mode of each trip on the tour.

Distribution of trips by trip mode and tour mode, which constrains the availability of each trip mode and influences the utility of each available trip mode.

Trip Mode Choice

Chart Column 2

Count vs Volume: All Day

Chart Column 2

Gap Statistics

Count vs Volume: EA

Chart Column 2

EA Gap Statistics

Count vs Volume: AM

Chart Column 2

AM Gap Statistics

Count vs Volume: MD

Chart Column 2

MD Gap Statistics

Count vs Volume: PM

Chart Column 2

PM Gap Statistics

Count vs Volume: EV

Chart Column 2

EV Gap Statistics

MTC vs MARIN

Chart Column 2

Volume Comparison - Daily

Volume Comparison - EA

Volume Comparison - AM

Volume Comparison - MD

Volume Comparison - PM

Volume Comparison - EV

Non-Mandatory Trip Flows

Chart Column 2

TLFD by District

TLFD by District

---
title: "`r paste(BASE_SCENARIO_NAME, 'vs.', BUILD_SCENARIO_NAME, 'Calibration Summary')`"
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: spacelab
    social: menu
    source_code: embed
runtime: shiny
---

```{r Setup}
opts_knit$set(root.dir = SYSTEM_APP_PATH)
knit_hooks$set(optipng = hook_optipng)
```

```{r setpar}
knitr::opts_knit$set(global.par = TRUE)
```


```{r ggplot_Theme}
theme_db <- theme_bw() + theme(plot.margin = unit(c(10,10,20,10),"pt"))
```

```{r Helper_Functions}
compare_bar_plotter <- function(base, build, base_name, build_name, xvar, yvar,
                        xlabel = xvar, ylabel = yvar, position = "dodge",
                        xrotate = FALSE, yrotate = FALSE, coord_flip = FALSE,
                        title = "", left_offset = 0, bottom_offset = 0){

  base$grp <- base_name
  build$grp <- build_name
  colnames(build) <- colnames(base)

  df <- rbind(base, build)

  p <- ggplot(df, aes_string(x = xvar, y = yvar)) +
    geom_bar(stat = "identity", aes(fill = grp), position = position) +
    xlab(xlabel) + ylab(ylabel) +
    labs(fill = "") +
    ggtitle(title) +
    theme(axis.text.x=element_text(angle=50, size=1, vjust=0.5)) +
    theme(axis.text.y=element_text(angle=50, size=1, vjust=0.5)) +
    theme_bw()

  if (xrotate) {
    p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
  }
  if (yrotate) {
    p <- p + theme(axis.text.y = element_text(angle = 45, hjust = 1))
  }
  if (coord_flip) {
    p <- p + coord_flip()
  }


  p <- plotly_build(p)
  p$layout$margin$l <- p$layout$margin$l+left_offset
  p$layout$margin$b <- p$layout$margin$b+bottom_offset
  return(p)

}

# This function combines two dataframes and returns a data frame with standard field names
# The field names in the two dataframes should be same and should be same as the variable
# names passed to the function
# input parameter - dataframe1, dataframe2, x variable, list of y variables
# renames x and y variables in standard form - xvar, (yvar1, yvar2),...
# Y variables are named in pairs - (yvar1, yvar2), (yvar3, yvar4), ....
# yvar1, yvar3, .. correspond to first dataframe and yvar2, yvar4, .. correspond to second dataframe
# computes proportions for each  y variable variable
get_standardDF <- function(data_df1, data_df2, x, y, grp = "", shared = F){

  #data_df1=base_df
  #data_df2=build_df
  #x="id"
  #y = c("freq_out", "freq_inb")
  #grp = "purpose"
  #shared = T
  #
  # create ID variable to join base and build data
  if(!shared){
    ev1 <- paste("data_df1$id_var <- data_df1$", x, sep = "")
    ev2 <- paste("data_df2$id_var <- data_df2$", x, sep = "")
    eval(parse(text = ev1))
    eval(parse(text = ev2))
  }else{
    if(grp==""){
      stop("group variable not specified")
    }else{
      ev1 <- paste("data_df1$id_var <- paste(data_df1$", grp, ", data_df1$", x, ', sep = "")', sep = "")
      ev2 <- paste("data_df2$id_var <- paste(data_df2$", grp, ", data_df2$", x, ', sep = "")', sep = "")
      eval(parse(text = ev1))
      eval(parse(text = ev2))
    }
  }

  data_df <- data_df1

  # rename variables to standard names
  names(data_df)[names(data_df) == x] <- 'xvar'
  if(shared){
    if(grp==""){
      stop("group variable not specified")
    }else{
      names(data_df)[names(data_df) == grp] <- 'grp_var'
    }
  }

  for(i in seq(from=1, to=length(y))){
    start_pos <- i*2-1
    yvar1 <- paste('yvar', start_pos, sep = "")
    yvar2 <- paste('yvar', start_pos+1, sep = "")
    names(data_df)[names(data_df) == y[[i]]] <- paste('yvar', start_pos, sep = "")
    eval_expr <- paste("data_df$", yvar2, " <- data_df2$", y[[i]], "[match(data_df$id_var, data_df2$id_var)]", sep = "")
    eval(parse(text = eval_expr))
  }
  
  data_df[is.na(data_df)] <- 0

  #data_df$grp_var <- as.character(data_df$grp_var)

  # compute proportions for y variables
  for(i in seq(from=1, to=length(y))){
    start_pos <- i*2-1
    prop_var1 <- paste('prop', start_pos, sep = "")
    y_var1    <- paste('yvar', start_pos, sep = "")
    prop_var2 <- paste('prop', start_pos+1, sep = "")
    y_var2    <- paste('yvar', start_pos+1, sep = "")
    if(shared){
      if(grp==""){
        stop("group variable not specified")
      }else{
        eval_expr1 <- paste("data_df <- data_df %>% group_by(grp_var) %>% mutate(", prop_var1, " = prop.table(", y_var1, "))", sep = "")
        eval_expr2 <- paste("data_df <- data_df %>% group_by(grp_var) %>% mutate(", prop_var2, " = prop.table(", y_var2, "))", sep = "")
      }
    }else{
      eval_expr1 <- paste("data_df <- data_df %>% mutate(", prop_var1, " = prop.table(", y_var1, "))", sep = "")
      eval_expr2 <- paste("data_df <- data_df %>% mutate(", prop_var2, " = prop.table(", y_var2, "))", sep = "")
    }

    eval(parse(text = eval_expr1))
    eval(parse(text = eval_expr2))
  }

  # set all NAs to zero
  # data_df[is.na(data_df)] <- 0

  if(!shared){
    return(data_df)
  }else{
    data_sd <- SharedData$new(data_df, ~grp_var)
    return(data_sd)
  }
}

# This function returns a SharedData object for creating comparison density plots
# input parameter - dataframe1, dataframe2, x variable, list of y variables,
# grouping variable, names of each run
# The function expects same field names across both dataframes
# renames x and y variables in standard form - xvar, yvar1, yvar2,...
# computes proportions for each  y variable variable for each group and run
# combines two dataframe and adds a run identifier
get_sharedData <- function(data_df1, data_df2, run1_name = 'base', run2_name = 'build',
                           x, y, grp){

  # rename variables to standard names
  names(data_df1)[names(data_df1) == x] <- 'xvar'
  names(data_df1)[names(data_df1) == grp] <- 'grp_var'
  for(i in 1:length(y)){
    names(data_df1)[names(data_df1) == y[[i]]] <- paste('yvar', i, sep = "")
  }

  names(data_df2)[names(data_df2) == x] <- 'xvar'
  names(data_df2)[names(data_df2) == grp] <- 'grp_var'
  for(i in 1:length(y)){
    names(data_df2)[names(data_df2) == y[[i]]] <- paste('yvar', i, sep = "")
  }

  # compute proportions for y variables
  data_df1 <- group_by(data_df1, grp_var)
  for(i in 1:length(y)){
    prop_var <- paste('prop', i, sep = "")
    y_var    <- paste('yvar', i, sep = "")
    eval_expr <- paste("data_df1 <- data_df1 %>% mutate(", prop_var, " = prop.table(", y_var, "))", sep = "")
    eval(parse(text = eval_expr))
  }

  data_df2 <- group_by(data_df2, grp_var)
  for(i in 1:length(y)){
    prop_var <- paste('prop', i, sep = "")
    y_var    <- paste('yvar', i, sep = "")
    eval_expr <- paste("data_df2 <- data_df2 %>% mutate(", prop_var, " = prop.table(", y_var, "))", sep = "")
    eval(parse(text = eval_expr))
  }

  # add run identifiers
  data_df1$run <- run1_name
  data_df2$run <- run2_name

  # combine dataframes
  data_df <- rbind(data_df1, data_df2)

  # set all NAs to zero
  data_df[is.na(data_df)] <- 0

  data_sd <- SharedData$new(data_df, ~grp_var)
  return(data_sd)
}

# This function returns bar plot for a given X-Y data frame
# The function expects the data frame columns to be named as
# xvar, yvar1, yvar2...
# function plots only two series at a time
# which two y series to plot is determined by the index variable
# index==1 :- yvar1, yvar2, index==2 :- yvar,3,4 and so on
# names of series to be plotted should also be passed as a list argument
# number of elements in names list determines the number of series to be added
plotly_bar_plotter <- function(data, type = 'bar', xlabel = "", ylabel = "", percent = FALSE,
                               title = "", height = 0, width = 0, ynames = c(""), left_offset = 0,
                               bottom_offset = 0, tickformat = "", hoverformat = "", tickangle = 0, index = 1, tickvals = c(), ticktext = c()){
  #initial setup
  start_pos <- 2*index - 1
  exp_tickvals <- ifelse(length(tickvals)>0, ', tickvals = tickvals', "")
  exp_ticktext <- ifelse(length(ticktext)>0, ', ticktext = ticktext', "")

  #generate plot
  if(!percent){
    ylab <- ifelse(ylabel=="", "Percent", ylabel)
    hformat <- ifelse(hoverformat=="", '.1f', hoverformat)
    eval_expr <- paste("p <- plot_ly(data, x = ~xvar, y = ~yvar", start_pos, ", type = type, name = ynames[[1]]) %>% ",
                       "add_trace(y = ~yvar", start_pos+1, ", name = ynames[[2]]) %>% ",
                       "layout(yaxis = list(hoverformat = hformat, title = ylab, tickformat = tickformat), xaxis = list(title = xlabel, tickangle = tickangle", exp_tickvals, exp_ticktext, "), barmode = 'group')", sep = "")
    eval(parse(text = eval_expr))
  }else{
    ylab <- ifelse(ylabel=="", "Frequency", ylabel)
    hformat <- ifelse(hoverformat=="", '.1%', hoverformat)
    eval_expr <- paste("p <- plot_ly(data, x = ~xvar, y = ~prop", start_pos, ", type = type, name = ynames[[1]]) %>% ",
                       "add_trace(y = ~prop", start_pos+1, ", name = ynames[[2]]) %>% ",
                       "layout(yaxis = list(hoverformat = hformat, title = ylab, tickformat = '%'), xaxis = list(title = xlabel, tickangle = tickangle", exp_tickvals, exp_ticktext,"), barmode = 'group')", sep = "")
    eval(parse(text = eval_expr))
  }

  p$x$layout$height <- height
  p$x$layout$width <- width
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  return(p)
}

# This function returns a spline plot with fill for a gievn X-Y dataframe
# The function expects the data frame columns to be named as
# x = ~xvar, y = (~yvar1 or prop1),  (~yvar2 or prop2) adn so on (Frequency or Percent),
# which y to use is determined by index parameter (one, two or three)
# and variable differentiating runs as ~run
# The function currebtly plots only one Y variables for each run
plotly_density_plotter <- function(data_df, index = "one", colors=c("orange", "steelblue"), fill = 'tozeroy',
                                   title = "", xlabel = "", ylabel = "", percent = T, alpha = 0.7, tickvals, ticktext, tickangle = 0,
                                   height=400, left_offset = 0, bottom_offset = 0, shape = 'spline', legend = T){
  ##standardize data frame
  #names(data_df)[names(data_df)==xvar]     <- 'xvar'
  #names(data_df)[names(data_df)==yvar]     <- 'yvar1'
  #names(data_df)[names(data_df)==prop_var] <- 'prop1'
  #names(data_df)[names(data_df)==grp]      <- 'run'

  # prepare plot using standardized dataframe
  if(percent){
    ylab <- ifelse(ylabel=="", "Percent", ylabel)

    p <- switch(index,
                "one" = plot_ly(data=data_df,x = ~xvar, y = ~prop1, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = "%"), showlegend = legend),
                "two" = plot_ly(data=data_df,x = ~xvar, y = ~prop2, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = "%"), showlegend = legend),
                "three" = plot_ly(data=data_df,x = ~xvar, y = ~prop3, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = "%"), showlegend = legend)
                )

  }else{
    ylab <- ifelse(ylabel=="", "Frequency", ylabel)

    p <- switch(index,
                "one" = plot_ly(data=data_df,x = ~xvar, y = ~yvar1, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend),
                "two" = plot_ly(data=data_df,x = ~xvar, y = ~yvar2, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend),
                "three" = plot_ly(data=data_df,x = ~xvar, y = ~yvar3, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend)
                )

    #p <- plot_ly(data=data_df,x = ~xvar, y = ~yvar1, colors=c("steelblue", "orange"), color = ~run, height=400, fill=fill) %>%
    #add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
    #layout(title = "",xaxis = list(title=xlabel), yaxis = list(title=ylab))
  }

  p$x$layout$height <- height
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  return(p)
}

# This function returns a pie chart
# Input is a 2 column data frame with a label variable and a value variable
plotly_pie_chart <- function(data, label_var, value_var, title = "",
                               height = 0, width = 0, left_offset = 0,bottom_offset = 0, top_offset = 0, shared = F){

  colors <- c('rgb(211,94,96)', 'rgb(128,133,133)', 'rgb(144,103,167)', 'rgb(171,104,87)', 'rgb(114,147,203)')

  if(!shared){
    names(data)[names(data)==label_var] <- 'label_var'
    names(data)[names(data)==value_var] <- 'value_var'

    p <- plot_ly(data, labels = ~label_var, values = ~value_var, type = 'pie',
          textposition = 'outside',
          textinfo = 'label+percent',
          insidetextfont = list(color = '#FFFFFF'),
          marker = list(colors = colors,
                        line = list(color = '#FFFFFF', width = 2)),
          showlegend = FALSE,
          sort = FALSE) %>%
    layout(title = title,
           xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
           yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
  }else{
    eval_expr <- paste("p <- plot_ly(data, labels = ~", label_var, ", values = ~", value_var, ", type = 'pie',
          textposition = 'outside',
          textinfo = 'label+percent',
          insidetextfont = list(color = '#FFFFFF'),
          marker = list(colors = colors,
                        line = list(color = '#FFFFFF', width = 2)),
          showlegend = FALSE,
          sort = FALSE) %>%
    layout(title = title,
           xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
           yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))", sep = "")

    eval(parse(text = eval_expr))
  }


  p$x$layout$height <- height
  p$x$layout$width <- width
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  p$x$layout$margin$t <- p$x$layout$margin$t + top_offset
  return(p)
}

lm_eqn <- function(df){
    m <- lm(y ~ x - 1, df);
    eq <- paste("Y = ", format(coef(m)[1], digits = 2), " * X , ", " r2 = ", format(summary(m)$r.squared, digits = 3), sep = "")
    return(eq)
}

```

Welcome
============================================

Summary {data-width=150}
--------------------------------------------

### About this Document

This document is an interactive dashboard viewable from most modern internet browsers. The dashboard is a validation and diagnostics tool for Activity Based Models. Users can compare model performance against a household survey as part of a validation exercise or compare two model runs for sensitivity testing. All of the data, charts, and maps viewable in this dashboard are embedded directly into the HTML file. An internet connection is necessary for the best user experience, but is not required.

Users may navigate to different areas of the dashboard using the navigation bar at the top of the page, and may interact directly with most tables, charts, and maps.

This document is best viewed using the most recent versions of the following web browsers:

* [Google Chrome](https://www.google.com/chrome/browser/desktop/)
* [Microsoft Internet Explorer](https://www.microsoft.com/en-us/download/internet-explorer.aspx)

Note: Mozilla Firefox does not correctly render the images in this HTML file.

Summary {data-width=600}
--------------------------------------------

### Modeling Region
```{r model_region}
# cat("Number of TAZs:", nrow(zone_shp))
# bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
# pal <- colorBin("YlOrRd", domain = zone_shp$HH, bins = bins)

m <- leaflet(data = zone_shp)%>%
 addTiles() %>%
 addProviderTiles(providers$OpenStreetMap, group = "Background Map") %>%
 addLayersControl(
   overlayGroups = "Background Map", options = layersControlOptions(collapsed = FALSE)
 ) %>%
 addPolygons(weight = 0.5, opacity = 1)
m

# m <- leaflet(data = zone_shp)%>%
#  addTiles() %>%
#  addProviderTiles(providers$OpenStreetMap, group = "Background Map") %>%
#  addLayersControl(
#    overlayGroups = "Background Map", options = layersControlOptions(collapsed = FALSE)
#  )
# m
#
```


Overview
============================================

Base Highlights {data-width=90}
--------------------------------------------

###

```{r Run_Date1_ValueBox}
sample_rate <- ifelse(IS_BASE_SURVEY=="Yes", "", as.character(BASE_SAMPLE_RATE*100))
valueBox(BASE_SCENARIO_NAME, paste("Sample Rate: ", sample_rate, "%", sep = ""), color = "DarkOrange")
base_pos <- which(base_csv_names=="totals")
base_df <- base_data[[base_pos]]
```

### Base Population
```{r Population1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_population"]/BASE_SAMPLE_RATE), big.mark = ","), "Population", icon = "ion-ios-people")
```

### Base Households
```{r Household1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_households"]/BASE_SAMPLE_RATE), big.mark = ","), "Households", icon = "glyphicon glyphicon-home")
```

### Base Tours
```{r Tours1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_tours"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Tours", icon = "ion-refresh")
```

### Base Trips
```{r Trips1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_trips"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Trips", icon = "ion-loop")
```

### Base Stops
```{r Stops1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_stops"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Stops", icon = "ion-ios-location")
```

### Base VMT
```{r VMT1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_vmt"]/BASE_SAMPLE_RATE), big.mark = ","), "Total VMT", icon = "ion-android-car")
```



Build Highlights {data-width=90}
--------------------------------------------

###

```{r Run_Date2_ValueBox}
valueBox(BUILD_SCENARIO_NAME, paste("Sample Rate: ", BUILD_SAMPLE_RATE*100, "%", sep = ""), color = "DarkOrange")
build_pos <- which(build_csv_names=="totals")
build_df <- build_data[[build_pos]]
```

### Build Population
```{r Population2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_population"]/BUILD_SAMPLE_RATE), big.mark = ","), "Population", icon = "ion-ios-people")
```

### Build Households
```{r Household2_ValueBox}
valueBox(prettyNum(format(round(build_df$value[build_df$name=="total_households"]/BUILD_SAMPLE_RATE), scientific=F), big.mark = ","), "Households", icon = "glyphicon glyphicon-home")
```

### Build Tours
```{r Tours2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_tours"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Tours", icon = "ion-refresh")
```

### Build Trips
```{r Trips2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_trips"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Trips", icon = "ion-loop")
```

### Build Stops
```{r Stops2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_stops"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Stops", icon = "ion-ios-location")
```

### Build VMT
```{r VMT2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_vmt"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total VMT", icon = "ion-android-car")
```


Chart Column 1 {data-width=275}
--------------------------------------------
### Person Type Distribution
```{r Chart_Person_Type}
base_pos <- which(base_csv_names=="pertypeDistbn")
base_df <- base_data[[base_pos]]
base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)]
base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char)
build_pos <- which(build_csv_names=="pertypeDistbn")
build_df <- build_data[[build_pos]]
build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)]
build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char)

colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "PERNAME", y = c("freq"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "Person Type", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, bottom_offset = 60, tickangle = -30)
p

```

### Household Size Distribution
```{r Chart_HHSize}
base_pos <- which(base_csv_names=="hhSizeDist")
base_df <- base_data[[base_pos]]

census_df = base_data[[which(base_csv_names=="hhSizeCensus")]]


build_pos <- which(build_csv_names=="hhSizeDist")
build_df <- build_data[[build_pos]]

colnames(census_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHSIZE", y = c("freq"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "HH Size", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)
p

```

Base Highlights2 {data-width=90}
--------------------------------------------

###

```{r Run_Date3_ValueBox}
valueBox(BASE_SCENARIO_NAME, "", color = "DarkOrange")
base_pos <- which(base_csv_names=="totals")
base_df <- base_data[[base_pos]]
```


### Tours per Person
```{r TourRate3_Gauge}
rate <- base_df$value[base_df$name=="total_tours"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Person
```{r TripRate3_Gauge}
rate <- base_df$value[base_df$name=="total_trips"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 5, gaugeSectors(danger = c(0,5), colors = c("Green", "Green", "Green")))
```

### Stops per Person
```{r StopRate3_Gauge}
rate <- base_df$value[base_df$name=="total_stops"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Household
```{r TRate3_Gauge}
rate <- base_df$value[base_df$name=="total_trips"]/base_df$value[base_df$name=="total_households"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 15, gaugeSectors(danger = c(0,15), colors = c("Green", "Green", "Green")))
```


Build Highlights2 {data-width=90}
--------------------------------------------

###

```{r Run_Date4_ValueBox}
valueBox(BUILD_SCENARIO_NAME, "", color = "DarkOrange")
build_pos <- which(build_csv_names=="totals")
build_df <- build_data[[build_pos]]
```


### Tours per Person
```{r TourRate4_Gauge}
rate <- build_df$value[build_df$name=="total_tours"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger =  c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Person
```{r TripRate4_Gauge}
rate <- build_df$value[build_df$name=="total_trips"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 5, gaugeSectors(danger = c(0,5), colors = c("Green", "Green", "Green")))
```

### Stops per Person
```{r StopRate4_Gauge}
rate <- build_df$value[build_df$name=="total_stops"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Household
```{r TRate4_Gauge}
rate <- build_df$value[build_df$name=="total_trips"]/build_df$value[build_df$name=="total_households"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 15, gaugeSectors(danger = c(0,15), colors = c("Green", "Green", "Green")))
```


Long Term Models{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=225}
--------------------------------------------


**Auto Ownership**

Results of household auto ownership model, which predicts number of vehicles per household.

**Mandatory TLFD**

Results of work and school location choice models.

Distribution of workers by distance between home and usual work place, and students by distance between home and school location.

Chart Column 1 {data-width=200}
--------------------------------------------
### Auto Ownership{data-height=265}
```{r Chart_Auto_Ownership}
if(IS_BASE_SURVEY=="Yes"){
  #cat("Census source: ", AO_CENSUS_LONG)
  # [3/31/2020] DH No census data used for SEMCOG
  census_df <- base_data[[which(base_csv_names=="autoOwnershipCensus")]]
  # base_df <- base_data[[which(base_csv_names=="autoOwnership")]]
  base_df <- base_data[[which(base_csv_names=="autoOwnership")]]

}else{
  base_df <- base_data[[which(base_csv_names=="autoOwnership")]]
}

build_df <- build_data[[which(build_csv_names=="autoOwnership")]]
build_df$X = NULL
base_df$X = NULL
#colnames(build_df) <- colnames(census_df)
#colnames(base_df) <- colnames(census_df)

base_df$HDISTRICT = levels(base_df$HDISTRICT)[base_df$HDISTRICT]
build_df$HDISTRICT = levels(build_df$HDISTRICT)[build_df$HDISTRICT]

# Limit to surveyed counties
build_df = build_df[build_df$HDISTRICT %in% base_df$HDISTRICT,]

sd.autoown <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHVEH", y = c("freq"), grp = "HDISTRICT", shared = T)

p <- plotly_bar_plotter(data = sd.autoown, xlabel = "Number of Vehicles", ylabel = "Percent", ynames = c(AO_CENSUS_LONG, BUILD_SCENARIO_NAME), percent = T, height = 225)

bscols(widths=c(3,9),
  list(
    filter_select("ao_cname", "Select County", sd.autoown, ~grp_var, multiple=F)),
    p
  )

#p
```

### {data-height=140}
```{r Gauge_WFH1}


```

### Percentage Working From Home{data-height=250}
```{r Chart_WFH}
base_df <- base_data[[which(base_csv_names=="wfh_summary")]]
base_df$share <- base_df$WFH/base_df$Workers
build_df <- build_data[[which(build_csv_names=="wfh_summary")]]
build_df$share <- build_df$WFH/build_df$Workers
build_df = build_df[which(str_to_upper(build_df$District) %in% str_to_upper(base_df$District)),]
std_DF <- cbind(base_df[,c("District", "share")], build_df[,c("share")])
colnames(std_DF) <- c("xvar", "prop1", "prop2")
p <- plotly_bar_plotter(data = std_DF, xlabel = "District", ylabel = "Percent WFH", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 275, tickangle = -320, bottom_offset = 25)
p

```



Chart Column 2 {data-width=350}
--------------------------------------------


### Mandatory TLFD{data-height=475}
```{r mandatoryTLFD}
base_df1 <- base_data[[which(base_csv_names=="workTLFD")]]
base_df1 <- melt(base_df1, id = c("distbin"))

base_df2 <- base_data[[which(base_csv_names=="univTLFD")]]
base_df2 <- melt(base_df2, id = c("distbin"))

base_df3 <- base_data[[which(base_csv_names=="schlTLFD")]]
base_df3 <- melt(base_df3, id = c("distbin"))

base_df <- cbind(base_df1, base_df2$value, base_df3$value)
colnames(base_df) <- c("distbin","variable","value1","value2","value3")

build_df1 <- build_data[[which(build_csv_names=="workTLFD")]]
build_df1 <- melt(build_df1, id = c("distbin"))

build_df2 <- build_data[[which(build_csv_names=="univTLFD")]]
build_df2 <- melt(build_df2, id = c("distbin"))

build_df3 <- build_data[[which(build_csv_names=="schlTLFD")]]
build_df3 <- melt(build_df3, id = c("distbin"))

build_df <- cbind(build_df1, build_df2$value, build_df3$value)
colnames(build_df) <- c("distbin","variable","value1","value2","value3")

sd.purpose <- get_sharedData(data_df1 = build_df, data_df2 = base_df, run1_name = BUILD_SCENARIO_NAME,
                             run2_name = BASE_SCENARIO_NAME, x = "distbin", y = c("value1", "value2", "value3"), grp = "variable")

p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Miles to Work", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)
p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Miles to University", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)
p3 <- plotly_density_plotter(sd.purpose, index = "three", xlabel = "Miles to School", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)

bscols(widths=c(12),
  list(filter_select("Purpose_County", "Select District", sd.purpose, ~grp_var,multiple=F),
  p1,
  p2,
  p3)
  )

```


Flows & Tour Lengths{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=135}
--------------------------------------------

**County-County Flow of Workers**

Crosstab of workers by home county and usual work place county.

Note: Districts can be Tract, County, District etc.

**Average Tour Lengths**

Average tour length to workplace by District of residence



Chart Column 1
--------------------------------------------

### {data-height=300}
```{r Table1_CountyFlows, eval  = TRUE}
cat("County - County Flow of Workers")

if(IS_BASE_SURVEY=="Yes"){
  base_df <- base_data[[which(base_csv_names=="countyFlows")]]
  # base_df <- base_data[[which(base_csv_names=="countyFlows")]]
}else{
  base_df <- base_data[[which(base_csv_names=="countyFlowsCensus")]]
}

base_df[,!colnames(base_df) %in% c("X")] <- base_df[,!colnames(base_df) %in% c("X")]/BASE_SAMPLE_RATE
t1 <- kable(base_df, format = 'html', caption = DISTRICT_FLOW_CENSUS, digits = 0, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10)
t1

```

### {data-height=280}
```{r Table1_MandTripLengths}
cat("Average Mandatory Tour Lengths")

base_df <- base_data[[which(base_csv_names=="mandTripLengths")]]
df <- base_df
colnames(df) <- c("Home District", "Work","University","School")

eval_expr <- paste("t1 <- kable(df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') %>%
  add_header_above(c(' ' = 1, '", BASE_SCENARIO_NAME, "' = 3))", sep = "")
eval(parse(text = eval_expr))
t1
```


Chart Column 1
--------------------------------------------
### {data-height=300}
```{r Table2_CountyFlows, eval = TRUE}
cat("County - County Flow of Workers")

build_pos <- which(build_csv_names=="countyFlows")
build_df <- build_data[[build_pos]]
build_df[,!colnames(build_df) %in% c("X")] <- build_df[,!colnames(build_df) %in% c("X")]/BUILD_SAMPLE_RATE
t2 <- kable(build_df, format = 'html', caption = BUILD_SCENARIO_NAME, digits = 0, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10)
t2
```

### {data-height=280}
```{r Table2_MandTripLengths}
cat("Average Mandatory Tour Lengths")

build_df <- build_data[[which(build_csv_names=="mandTourLengths")]]
df <- build_df
colnames(df) <- c("Home District", "Work","University","School")

eval_expr <- paste("t2 <- kable(df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') %>%
  add_header_above(c(' ' = 1, '", BUILD_SCENARIO_NAME, "' = 3))", sep = "")
eval(parse(text = eval_expr))
t2
```

Employment vs Workers{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=175}
--------------------------------------------

********

**Employment vs Workers comparison at TAZ level**

Results of work location model.

Comparison of assigned workers to available employment at TAZ level.

Only for build scenario.


Chart Column 2{.tabset}
--------------------------------------------

### By County{data-height=575} 

```{r job_wrk1a}
#knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workersByCounty.jpeg"))
# knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workersByTAZ_County.jpeg"))

#workers_per_co.csv
#workers_per_taz.csv

base_df <- build_data[[which(build_csv_names=="employees_per_co")]]
build_df <- build_data[[which(build_csv_names=="workers_per_co")]]

base_df$WDISTRICT = levels(base_df$WDISTRICT)[base_df$WDISTRICT]
build_df$WDISTRICT = levels(build_df$WDISTRICT)[build_df$WDISTRICT]
build_df = build_df[build_df$WDISTRICT %in% base_df$WDISTRICT,]
base_df = base_df[base_df$WDISTRICT %in% build_df$WDISTRICT,]

#colnames(base_df)[which(colnames(base_df) == "freq")] = BASE_SCENARIO_NAME
#colnames(build_df)[which(colnames(build_df) == "freq")] = BUILD_SCENARIO_NAME

df = base_df
df$freq_build = build_df$freq[which(df$WDISTRICT == build_df$WDISTRICT)]/BUILD_SAMPLE_RATE

co_cor = cor(df$freq, df$freq_build)^2
co_lm = lm(freq_build ~ freq, df)

text1 = paste("r2 =", round(co_cor, 4))
text2 = paste("y = ", round(co_lm$coefficients[['freq']], 4), "x  + ", round(co_lm$coefficients[['(Intercept)']], 2), sep = "")

ggplot(df, aes(x = freq, y = freq_build, label = WDISTRICT)) + geom_point() +
  geom_abline(slope = 1, intercept = 0, color = "red") +
  geom_text(vjust = 0, nudge_y = 0.1, check_overlap = TRUE) +
  scale_x_log10("SEDATA Input", labels = comma, limits = c(10000, max(df$freq, df$build_freq))) +
  scale_y_log10("ActivitySim", labels = comma, limits = c(10000, max(df$freq, df$build_freq))) +
  annotate("text", x=500000, y=c(20000, 15000), label= c(text1, text2))


```

### By TAZ{data-height=575}
```{r job_wrk_taz}
 #knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workersByTAZ.jpeg"))
base_df <- build_data[[which(build_csv_names=="employees_per_taz")]]
build_df <- build_data[[which(build_csv_names=="workers_per_taz")]]


df = data.frame(taz = 1:3632) %>%
  dplyr::left_join(base_df, by = "taz") %>%
  dplyr::left_join(build_df, by = c("taz" = "worktaz")) %>%
  rename(base = freq.x) %>%
  mutate(build = freq.y/BUILD_SAMPLE_RATE) %>%
  mutate(build = ifelse(is.na(build), 0, build),
         base = ifelse(is.na(base), 0, base))

co_cor = cor(df$base, df$build)^2
co_lm = lm(build ~ base, df)

text1 = paste("r2 =", round(co_cor, 4))
text2 = paste("y = ", round(co_lm$coefficients[['base']], 4), "x  + ", round(co_lm$coefficients[['(Intercept)']], 2), sep = "")

ggplot(df, aes(x = base, y = build)) + geom_point(alpha = 0.3) +
  geom_abline(slope = 1, intercept = 0, color = "red") +
  scale_x_continuous("SEDATA Input", labels = comma, limits = c(0, max(df$freq, df$build_freq))) +
  scale_y_continuous("ActivitySim", labels = comma, limits = c(0, max(df$freq, df$build_freq))) +
  annotate("text", x=60000, y=c(4000, 1500), label= c(text1, text2))
```

### Low Income{data-height=575}
```{r job_wrk2}

#knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workersByEmpCat_inc1.jpeg"))


# inc1_plots <- paste0(list.files(SYSTEM_JPEG_PATH, "inc1_", full.names=TRUE))
# # inc1_plot_names <- str_replace_all(inc1_plots, c("workers_inc1_" = "", SYSTEM_JPEG_PATH = "", ".jpeg"= ""))
# cat(inc1_plots)
# cat(SYSTEM_JPEG_PATH)
# selectInput('workers_inc1', 'Employment Category', inc1_plots, selected="total")
# renderImage(input$workers_inc1, deleteFile=FALSE)


# bsselect(inc1_plots, type = "img", selected = "tot", live_search = TRUE, show_tick = TRUE)
```

### Medium Income{data-height=575}
```{r job_wrk3}

#knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workersByEmpCat_inc2.jpeg"))

```

### High Income{data-height=575}
```{r job_wrk4}

#knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workersByEmpCat_inc3.jpeg"))

```

### Very High Income{data-height=575}
```{r job_wrk5}

#knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workersByEmpCat_inc4.jpeg"))

```

### Total{data-height=575}
```{r job_wrk7}

#knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "workers_incAll.jpeg"))

```


Zero Auto Households {data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=175}
--------------------------------------------

********

**Census vs Model comparison at Census Tract level**

Compares number of zero auto households.

Census Data is from 2017 five year ACS.

Only for build scenario.

Summary
--------------------------------------------

### Zero Auto Households Census vs Model
```{r zero_auto_hh}

# BEWARE! shp data names are shortend by writeOGR in the script that creates the ct_zero_auto_shp file
#  Names in that script won't match names shown here.
#  For example, ct_zero_auto_shp@data$Diff_ZeroAuto ->  ct_zero_auto_shp@data$Dff_ZrA
labels <- sprintf(
  "%s
%s %.2f %s
%s", ct_zero_auto_shp@data$TRACTCE, ct_zero_auto_shp@data$txtCmm2, ct_zero_auto_shp@data$Dff_ZrA, "%", ct_zero_auto_shp@data$txtCmm1) %>% lapply(htmltools::HTML) bins <- c(-Inf, -100, -75, -50, -25, -5, 5, 25, 50, 75, 100, Inf) pal <- colorBin("PiYG", domain = ct_zero_auto_shp@data$Dff_ZrA, na.color="transparent", bins = bins) m <- leaflet(data = ct_zero_auto_shp)%>% addTiles() %>% addProviderTiles(providers$OpenStreetMap, group = "Background Map") %>% addLayersControl( overlayGroups = "Background Map", options = layersControlOptions(collapsed = FALSE) ) %>% addPolygons(group='ZeroCarDiff', fillColor = ~pal(Dff_ZrA), weight = 0.2, opacity = 1, color = "gray", stroke=T, dashArray = "5, 1", fillOpacity = 0.7, highlight = highlightOptions( weight = 1, color = "blue", dashArray = "", fillOpacity = 0.7, bringToFront = TRUE), label = labels, labelOptions = labelOptions( style = list("font-weight" = "normal", padding = "3px 8px"), textsize = "15px", direction = "auto")) %>% addLegend(pal = pal, values = ~density, opacity = 0.7, title = "Estimated(%) - Observed(%) Bins", position = "bottomright") m ``` Tour Summaries{data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=225} -------------------------------------------- This page summarizes day-pattern and tour generation model results. **Daily Activity Pattern** Results of Coordinated Daily Activity Pattern (CDAP) model, summarized for each person. _M_ : One or more mandatory tours _N_ : No mandatory tours but one or more non-mandatory tours _H_ : No tours (either home all day or out of area) **Percentage of Households with Joint Tour** Also the result of the CDAP model, summarized for each household. **Mandatory Tour Frequency** Result of the mandatory tour frequency model, summarized for each person with a daily activity pattern type _M_ **Tour rate by person type** Summary of tours per person resulting from all tour generation models. Joint tours are counted for each participant. **Individual non-mandatory tour frequency** Results of individual non-mandatory tour frequency model, summarized for each person with a daily activity pattern type _M_ or _N_. Chart Column 1 {data-width=160} -------------------------------------------- ### Daily Activity Pattern{data-height=500} ```{r Hist_DAP} base_df <- base_data[[which(base_csv_names=="dapSummary_vis")]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) base_df$DAP <- factor(base_df$DAP, levels = dap_types) build_df <- build_data[[which(build_csv_names=="dapSummary_vis")]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) build_df$DAP <- factor(build_df$DAP, levels = dap_types) base_df$grp <- BASE_SCENARIO_NAME build_df$grp <- BUILD_SCENARIO_NAME colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="DAP", y = c("freq"), grp = "PERNAME", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 250, xlabel = "DAP", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("pertype_dap", "Select Person Type", sd.pertype, ~grp_var,multiple=F)), p ) ``` ### Percentage of Households with a Joint Tour{data-height=300} ```{r Hist_Presence_Joint} base_pos <- which(base_csv_names=="hhsizeJoint") base_df <- base_data[[base_pos]] base_df <- base_df %>% group_by(HHSIZE) %>% mutate(percent = prop.table(freq)) %>% filter(JOINT==1) %>% ungroup() build_pos <- which(build_csv_names=="hhsizeJoint") build_df <- build_data[[build_pos]] build_df <- build_df %>% group_by(HHSIZE) %>% mutate(percent = prop.table(freq)) %>% filter(JOINT==1) %>% ungroup() colnames(build_df) <- colnames(base_df) std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHSIZE", y = c("percent")) p <- plotly_bar_plotter(data = std_DF, xlabel = "HH Size", ylabel = "Percent of Households", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = F, tickformat = "%", hoverformat = ".1%") p ``` ### Mandatory Tour Frequency{data-height=500} ```{r Hist_MTF} base_pos <- which(base_csv_names=="mtfSummary_vis") base_df <- base_data[[base_pos]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) base_df$mtf_name <- mtf_df$name[match(base_df$MTF, mtf_df$code)] base_df$mtf_name <- factor(base_df$mtf_name, levels = mtf_names) build_pos <- which(build_csv_names=="mtfSummary_vis") build_df <- build_data[[build_pos]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) build_df$mtf_name <- mtf_df$name[match(build_df$MTF, mtf_df$code)] build_df$mtf_name <- factor(build_df$mtf_name, levels = mtf_names) colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="mtf_name", y = c("freq"), grp = "PERNAME", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 250, xlabel = "MTF Choice", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickangle = -30, bottom_offset = 50) bscols(widths=c(3,9), list( filter_select("pertype_mtf", "Select Person Type", sd.pertype, ~grp_var,multiple=F)), p ) ``` Chart Column 1 {data-width=150} -------------------------------------------- ### Total Tour Rate (only active Persons) ```{r Hist_totaltours} base_df <- base_data[[which(base_csv_names=="total_tours_by_pertype_vis")]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) base_df1 <- base_data[[which(base_csv_names=="activePertypeDistbn")]] base_df$persons <- base_df1$freq[match(base_df$PERTYPE, base_df1$PERTYPE)] base_df$tourrate <- round(base_df$freq/base_df$persons,2) build_df <- build_data[[which(build_csv_names=="total_tours_by_pertype_vis")]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) build_df1 <- build_data[[which(build_csv_names=="activePertypeDistbn")]] build_df$persons <- build_df1$freq[match(build_df$PERTYPE, build_df1$PERTYPE)] build_df$tourrate <- round(build_df$freq/build_df$persons,2) colnames(build_df) <- colnames(base_df) std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "PERNAME", y = c("tourrate")) p <- plotly_bar_plotter(data = std_DF, xlabel = "Person Type", ylabel = "Tour Rate", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = F, height = 340, tickangle = -30, bottom_offset = 50) p ``` ### Persons by Individual Non-Mandatory Tours ```{r Hist_INM} base_df <- base_data[[which(base_csv_names=="inmSummary_vis")]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) build_df <- build_data[[which(build_csv_names=="inmSummary_vis")]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nmtours", y = c("freq"), grp = "PERNAME", shared = T) #p <- plotly_bar_plotter(data = sd.pertype, height = 340, xlabel = "Number of Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, #BUILD_SCENARIO_NAME), percent = T, tickvals = c(seq(0,2), "3pl"), ticktext = c("0", "1", "2", "3pl")) p <- plotly_bar_plotter(data = sd.pertype, height = 340, xlabel = "Number of Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("pertype_mtf", "Select Person Type", sd.pertype, ~grp_var,multiple=F)), p ) ``` Joint Tours{data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=225} -------------------------------------------- ******** This page tabulates the results of the Joint Tour Frequency and Composition Model and the Joint Tour Person Participation Model. **Joint Tour Frequency** The frequency of households by number and purpose of joint tours. **Joint Tour Composition** The frequency of tours by composition (Adults only, Children only, Adults + Children). **Joint Tour Party Size** The frequency of joint tours by the number of household members participating in the tour. **Joint Tours by HH Size** The frequency of households by household size and the number of joint tours per household. **Joint Tours by HH Size** _Tour Level_ Distribution of joint tours by party size for each composition type. Chart Column 1 {data-width=150} -------------------------------------------- ### Joint Tour Frequency{data-height=675} ```{r jtf} base_df <- base_data[[which(base_csv_names=="jtf")]] build_df <- build_data[[which(build_csv_names=="jtf")]] # remove no joint tours option base_df <- base_df[-1,] build_df <- build_df[-1,] colnames(build_df) <- colnames(base_df) std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "alt_name", y = c("freq")) std_DF$xvar <- factor(std_DF$xvar, levels = jtf_alternatives) p <- plotly_bar_plotter(data = std_DF, xlabel = "Joint Tour Combination", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 500, bottom_offset = 275, tickangle = 300) p ``` ### Joint Tour Composition ```{r jtf_comp} base_df <- base_data[[which(base_csv_names=="jointComp")]] names(base_df)[names(base_df)=="tour_composition"] <- "COMPOSITION" build_df <- build_data[[which(build_csv_names=="jointComp")]] colnames(build_df) <- colnames(base_df) p1 <- plotly_pie_chart(data = base_df, label_var = "COMPOSITION", value_var = "freq", height = 250, title = BASE_SCENARIO_NAME, top_offset = 50) p2 <- plotly_pie_chart(data = build_df, label_var = "COMPOSITION", value_var = "freq", height = 250, title = BUILD_SCENARIO_NAME, top_offset = 50) bscols(widths=c(6,6), p1, p2 ) ``` Chart Column 1 {data-width=150} -------------------------------------------- ### Joint Tours By Number of Household Members ```{r jtf_partysize} base_df <- base_data[[which(base_csv_names=="jointPartySize")]] build_df <- build_data[[which(build_csv_names=="jointPartySize")]] colnames(build_df) <- colnames(base_df) build_df$freq[build_df$NUMBER_HH==5] <- sum(build_df$freq[build_df$NUMBER_HH>=5]) build_df <- build_df[build_df$NUMBER_HH<=5, ] std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "NUMBER_HH", y = c("freq")) p <- plotly_bar_plotter(data = std_DF, xlabel = "Joint Party Size", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 200) p ``` ### Joint Tours by Household Size ```{r jtf_byhhsize} base_pos <- which(base_csv_names=="jointToursHHSize") base_df <- base_data[[base_pos]] build_pos <- which(build_csv_names=="jointToursHHSize") build_df <- build_data[[build_pos]] colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="jointTours", y = c("freq"), grp = "hhsize", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 225, xlabel = "Number of Joint Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("jtf_hhsize", "Select HH Size Group", sd.pertype, ~grp_var,multiple=F)), p ) ``` ### Party Size Distribution by Joint Tour Composition ```{r jtf_comppartysize} base_df <- base_data[[which(base_csv_names=="jointCompPartySize")]] build_df <- build_data[[which(build_csv_names=="jointCompPartySize")]] colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="partysize", y = c("freq"), grp = "comp", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 225, xlabel = "Joint Party Size", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("jtf_comp", "Select Party Composition", sd.pertype, ~grp_var,multiple=F)), p ) ``` Destination{data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=225} -------------------------------------------- ******** **Non-Mandatory Tour Length Distribution** Results of non-mandatory tour destination choice models. Distribution of tours by distance between tour origin and destination for each non-mandatory tour purpose. Chart Column 1 {data-width=100} -------------------------------------------- ### Non-Mandatory Tour Length Distribution{data-height=350} ```{r nm_tlfd} base_df <- base_data[[which(base_csv_names=="tourDistProfile_vis")]] build_df <- build_data[[which(build_csv_names=="tourDistProfile_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$PURPOSE <- as.character(base_df$PURPOSE) build_df$PURPOSE <- as.character(build_df$PURPOSE) base_df$PURPOSE <- purpose_type_df$name[match(base_df$PURPOSE, purpose_type_df$code)] build_df$PURPOSE <- purpose_type_df$name[match(build_df$PURPOSE, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "distbin", y = c("freq"), grp = "PURPOSE") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Miles", percent = T, tickvals = seq(2,41), ticktext = c(seq(1,40), "40pl")) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), p1 ) ``` ### Average Non-Mandatory Tour Lengths (Miles){data-height=250} ```{r Table1_nonMandTripLength} base_df <- base_data[[which(base_csv_names=="nonMandTripLengths")]] build_df <- build_data[[which(build_csv_names=="nonMandTourLengths")]] df <- data.frame(base_df, build_df[,-1]) colnames(df) <- c("Purpose", BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME) df$Purpose <- purpose_type_df$name[match(df$Purpose, purpose_type_df$code)] t1 <- kable(df, format = "html", digits = 2, row.names = F, align = 'c', format.args = list(big.mark = ',')) %>% kable_styling("striped", full_width = F) t1 ``` TOD {data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=200} -------------------------------------------- ******** **Tour Departure Arrival & Duration** Tour Time-of-day Choice Model results. Each tour is assigned a time period of departure (time leaving home or work) and arrival (time arriving back at home or work). The entire day is divided into 18 one-hour bins (the first bin includes 3:00 AM to 6:00 AM and the last bin includes 11:00 PM to 3:00 AM). Tour duration is calculated as a function of departure and arrival period. It includes travel time and time spent at the primary destination and all intermediate stops. Results are shown for tours, filtered by tour purpose. ******** Chart Column 1 {.tabset} -------------------------------------------- ### Tour Departure-Arrival Profile ```{r tour_tod} base_df <- base_data[[which(base_csv_names=="todProfile_vis")]] base_df$tod_bin <- tod_df$bin[match(base_df$id, tod_df$id)] base_df$dur_bin <- dur_df$bin[match(base_df$id, dur_df$id)] build_df <- build_data[[which(build_csv_names=="todProfile_vis")]] build_df$tod_bin <- tod_df$bin[match(build_df$id, tod_df$id)] build_df$dur_bin <- dur_df$bin[match(build_df$id, dur_df$id)] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "id", y = c("freq_dep", "freq_arr", "freq_dur"), grp = "purpose") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Tour Departure", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 275) p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Tour Arrival", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 275) p3 <- plotly_density_plotter(sd.purpose, index = "three", xlabel = "Tour Duration", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = durBins, bottom_offset = 50, tickangle = 315, height = 225) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2, p3) ) ``` ### Tour Aggregate Departure-Arrival Profile ```{r tour_tod_agg} base_df <- base_data[[which(base_csv_names=="todProfile_vis")]] base_df$tod_agg <- cut(base_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) base_df$tod_agg <- factor(base_df$tod_agg, levels = timePeriodOrder) base_df <- base_df %>% dplyr::group_by(purpose, tod_agg) %>% dplyr::summarise(freq_dep = sum(freq_dep), freq_arr = sum(freq_arr), freq_dur = sum(freq_dur)) %>% dplyr::ungroup() base_df = base_df %>% arrange(match(tod_agg, timePeriodOrder)) build_df <- build_data[[which(build_csv_names=="todProfile_vis")]] build_df$tod_agg <- cut(build_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) build_df$tod_agg <- factor(build_df$tod_agg, levels = timePeriodOrder) build_df <- build_df %>% dplyr::group_by(purpose, tod_agg) %>% dplyr::summarise(freq_dep = sum(freq_dep), freq_arr = sum(freq_arr), freq_dur = sum(freq_dur)) %>% dplyr::ungroup() build_df = build_df %>% arrange(match(tod_agg, timePeriodOrder)) colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tod_agg", y = c("freq_dep", "freq_arr", "freq_dur"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Tour Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) p2 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Tour Arrival", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, index = 2) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2) ) ``` Tour Mode{data-navmenu="Tour Level"} ============================================ Chart Column 1{data-width=150} -------------------------------------------- ### Tour Mode Choice ```{r tourMode} # [3/31/2020] DH if(BUILD_SCENARIO_NAME == "CMAP_OBS") { # if running HTS vs OBS mode comparison base_df <- base_data[[which(base_csv_names=="tmodeProfile_vis_HTS")]] build_df <- build_data[[which(build_csv_names=="tmodeProfile_vis_calib")]] } else { base_df <- base_data[[which(base_csv_names=="tmodeProfile_vis_calib")]] build_df <- build_data[[which(build_csv_names=="tmodeProfile_vis")]] } build_df$purpose <- as.character(build_df$purpose) build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] colnames(build_df) <- colnames(base_df) base_df$purpose <- as.character(base_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="id", y = c("freq_as0", "freq_as1", "freq_as2", "freq_all"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Zero Auto]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, bottom_offset = 55, tickangle = 300) p2 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Autos < Workers]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, index = 2, bottom_offset = 55, tickangle = 300) p3 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Autos >= Workers]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, index = 3, bottom_offset = 55, tickangle = 300) p4 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Total]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, index = 4, bottom_offset = 55, tickangle = 300) filter_select("tourMode", "Select Tour Purpose", sd.pertype, ~grp_var,multiple=F) ``` ******** **Tour Mode Choice** Results of Tour Mode Choice Models, which selects a primary mode for each tour. Distribution of tours by tour mode and the ratio of autos to drivers in the household. Chart Column 2 {data-width=400} -------------------------------------------- ### ```{r tourMode2} bscols(widths=c(12), list(p1,p2) ) ``` Chart Column 3 {data-width=400} -------------------------------------------- ### ```{r tourMode3} bscols(widths=c(12), list(p3,p4) ) ``` Stop Frequency {data-navmenu="Trip Level"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Stop Frequency** Results of the Intermediate Stop Frequency Model, which predicts the number of intermediate stops on each tour by tour direction (outbound versus inbound). The summary shows percent of tours by number of stops on the tour and tour direction. **Stop Purpose** Results of the Intermediate Stop Purpose Model, which is currently implemented as a Monte Carlo choice according to probability distributions generated from survey data. The summary shows the percent of intermediate stops by stop purpose and tour purpose. Chart Column 1 {data-width=200} -------------------------------------------- ### Stop Frequency - Directional ```{r stopfreq_dir} base_df <- base_data[[which(base_csv_names=="stopfreqDir_vis")]] build_df <- build_data[[which(build_csv_names=="stopfreqDir_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.pertype1 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nstops", y = c("freq_out", "freq_inb"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype1, height = 325, xlabel = "Number of Stops - Outbound", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,4), ticktext = c("0", "1", "2", "3pl")) p2 <- plotly_bar_plotter(data = sd.pertype1, height = 325, xlabel = "Number of Stops - Inbound", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,4), ticktext = c("0", "1", "2", "3pl"), index = 2) bscols(widths=c(12), list( filter_select("stopfreq_dir", "Select Tour Purpose", sd.pertype1, ~grp_var,multiple=F), p1, p2) ) ``` Chart Column 1 {data-width=300} -------------------------------------------- ### Stop Frequency - Total{data-height=250} ```{r stopfreq_total} base_df <- base_data[[which(base_csv_names=="stopfreq_total_vis")]] build_df <- build_data[[which(build_csv_names=="stopfreq_total_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.pertype2 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nstops", y = c("freq"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype2, height = 350, xlabel = "Number of Stops", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,7), ticktext = c("0", "1", "2", "3", "4", "5", "6pl")) bscols(widths=c(3,9), list( filter_select("stopfreq_total", "Select Tour Purpose", sd.pertype2, ~grp_var,multiple=F)), p1 ) ``` ### Stop Purpose by Tour Purpose{data-height=250} ```{r stoppurp_tourpurp} base_df <- base_data[[which(base_csv_names=="stoppurpose_tourpurpose_vis")]] build_df <- build_data[[which(build_csv_names=="stoppurpose_tourpurpose_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.pertype3 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="stop_purp", y = c("freq"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype3, height = 350, xlabel = "Stop Purpose", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,9), ticktext = stopPurposes[1:9]) bscols(widths=c(3,9), list( filter_select("stoppurp_tourpurp", "Select Tour Purpose", sd.pertype3, ~grp_var,multiple=F)), p1 ) ``` Location{data-navmenu="Trip Level"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Stop Location** Results of the Intermediate Stop Location Choice Model, which predicts the location of each intermediate stop. The summary shows the distribution of intermediate stops by out of direction distance and tour purpose. Out of direction distance is defined as the extra distance to the destination as a result of traveling through the stop location. For stops in the outbound direction, it is based on the distance between the last known location (the tour origin or previous outbound stop) and the tour primary destination. For stops in the inbound direction, it is based on the distance between the last known location (the tour primary destination or previous inbound stop) and the tour origin. Chart Column 1 {data-width=800} -------------------------------------------- ### Stop Location - Out of Direction Distance{data-height=350} ```{r stopDC} base_df <- base_data[[which(base_csv_names=="stopDC_vis")]] build_df <- build_data[[which(build_csv_names=="stopDC_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$PURPOSE <- as.character(base_df$PURPOSE) build_df$PURPOSE <- as.character(build_df$PURPOSE) base_df$PURPOSE <- purpose_type_df$name[match(base_df$PURPOSE, purpose_type_df$code)] build_df$PURPOSE <- purpose_type_df$name[match(build_df$PURPOSE, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "distbin", y = c("freq"), grp = "PURPOSE") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Out of Direction Distance (Miles)", percent = T, left_offset = 25, tickvals = seq(1,42), ticktext = outDirDist, height = 600, tickangle = 300, bottom_offset = 50) bscols(widths=c(12), list( filter_select("stopDC", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), p1) ) ``` Chart Column 1 {data-width=300} -------------------------------------------- ### Average Out of Direction Distance (Miles){data-height=250} ```{r Table1_outOfDir} base_df <- base_data[[which(base_csv_names=="avgStopOutofDirectionDist_vis")]] build_df <- build_data[[which(build_csv_names=="avgStopOutofDirectionDist_vis")]] df <- data.frame(base_df, build_df[,-1]) colnames(df) <- c("Tour_Purpose", BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME) df$Tour_Purpose <- purpose_type_df$name[match(df$Tour_Purpose, purpose_type_df$code)] # #t1 <- kable(df, format = "html", digits = 2, row.names = F, align = 'c', format.args = list(big.mark = ',')) %>% # kable_styling("striped", full_width = F) t1 <- htmlTable(txtRound(df, 2), align = "c|r", rnames = F, col.columns = c(rep("#E6E6F0", 1), rep("none", ncol(df) - 1)), caption = "_______________________________________________________") t1 ``` TOD{data-navmenu="Trip Level"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Stop Departure** Results of the Stop Departure Time Choice Model. The departure time of each stop on the tour is currently implemented as a Monte Carlo choice of time period from distributions generated from survey data. The entire day is divided into 18 one-hour bins (The first bin includes 3:00 AM to 6:00 AM and the last bin includes 11:00 PM to 3:00 AM). **Trip Departure** Summarizes all trips by departure time period, including trips to and from intermediate stops and the tour primary destination. Chart Column 1 {.tabset} -------------------------------------------- ### Stop & Trip Departure{data-height=650} ```{r stopDep} base_df <- base_data[[which(base_csv_names=="stopTripDep_vis")]] build_df <- build_data[[which(build_csv_names=="stopTripDep_vis")]] colnames(base_df) <- c("timebin", "purpose", "freq_stop", "freq_trip") colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "timebin", y = c("freq_stop", "freq_trip"), grp = "purpose") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Stop Departure", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 400) p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Trip Departure", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 400) #p3 <- datatable(sd.purpose$data()) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2) ) ``` ### Aggregate Stop & Trip Departure ```{r trip_tod_agg} base_df <- base_data[[which(base_csv_names=="stopTripDep_vis")]] colnames(base_df) <- c("id","purpose","freq_stop","freq_trip") base_df$tod_agg <- cut(base_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) base_df$tod_agg <- factor(base_df$tod_agg, levels = timePeriodOrder) base_df <- base_df %>% group_by(purpose, tod_agg) %>% dplyr::summarise(freq_stop = sum(freq_stop), freq_trip = sum(freq_trip)) %>% ungroup() build_df <- build_data[[which(build_csv_names=="stopTripDep_vis")]] colnames(build_df) <- c("id","purpose","freq_stop","freq_trip") build_df$tod_agg <- cut(build_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) build_df$tod_agg <- factor(build_df$tod_agg, levels = timePeriodOrder) build_df <- build_df %>% group_by(purpose, tod_agg) %>% dplyr::summarise(freq_stop = sum(freq_stop), freq_trip = sum(freq_trip)) %>% ungroup() colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tod_agg", y = c("freq_stop", "freq_trip"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Stop Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) p2 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Trip Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, index = 2) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2) ) ``` Trip Mode{data-navmenu="Trip Level"} ============================================ Chart Column 1 {data-width=125} -------------------------------------------- ### {data-height=200} ***Trip Mode Choice*** The results of the Trip Mode Choice Model, which predicts the mode of each trip on the tour. Distribution of trips by trip mode and tour mode, which constrains the availability of each trip mode and influences the utility of each available trip mode. ### Trip Mode Choice ```{r tripMode} if(BUILD_SCENARIO_NAME == 'CMAP_OBS') { base_df <- base_data[[which(base_csv_names=="tripModeProfile_vis_HTS")]] build_df <- build_data[[which(build_csv_names=="tripModeProfile_vis_calib")]] } else { build_df <- build_data[[which(build_csv_names=="tripModeProfile_vis")]] base_df <- base_data[[which(base_csv_names=="tripModeProfile_vis_calib")]] } colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tripmode", y = c("value"), grp = "grp_var", shared = T) p <- plotly_bar_plotter(data = sd.purpose, height = 700, xlabel = "Trip Mode", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tripMode)), ticktext = tripMode, bottom_offset = 75) bscols(widths=c(12), list(filter_select("tripMode1", "Select Tour Purpose", sd.purpose, ~purpose,multiple=F), filter_select("tripMode1", "Select Tour Mode", sd.purpose, ~tourmode,multiple=F)) ) ``` Chart Column 2 {data-width=800} -------------------------------------------- ### ```{r tripMode2} bscols(widths=c(12), list(p) ) ``` Count vs Volume: All Day{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### Count vs Volume - All Links{data-height=575} ```{r count_vol2} ``` ### Gap Statistics{data-height=575} ```{r count_vol3} ``` Count vs Volume: EA{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### EA Count vs Volume - All Links{data-height=575} ```{r count_vol4} ``` ### EA Gap Statistics{data-height=575} ```{r count_vol5} ``` Count vs Volume: AM{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### AM Count vs Volume - All Links{data-height=575} ```{r count_vol6} ``` ### AM Gap Statistics{data-height=575} ```{r count_vol7} ``` Count vs Volume: MD{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### MD Count vs Volume - All Links{data-height=575} ```{r count_vol8} ``` ### MD Gap Statistics{data-height=575} ```{r count_vol9} ``` Count vs Volume: PM{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### PM Count vs Volume - All Links{data-height=575} ```{r count_vol10} ``` ### PM Gap Statistics{data-height=575} ```{r count_vol11} ``` Count vs Volume: EV{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### EV Count vs Volume - All Links{data-height=575} ```{r count_vol12} ``` ### EV Gap Statistics{data-height=575} ```{r count_vol13} ``` MTC vs MARIN {data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### Volume Comparison - Daily{data-height=575} ```{r count_vol14} #knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "assign_summary_Daily.jpeg")) ``` ### Volume Comparison - EA{data-height=575} ```{r count_vol15} #knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "assign_summary_EA.jpeg")) ``` ### Volume Comparison - AM{data-height=575} ```{r count_vol16} #knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "assign_summary_AM.jpeg")) ``` ### Volume Comparison - MD{data-height=575} ```{r count_vol17} #knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "assign_summary_MD.jpeg")) ``` ### Volume Comparison - PM{data-height=575} ```{r count_vol18} #knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "assign_summary_PM.jpeg")) ``` ### Volume Comparison - EV{data-height=575} ```{r count_vol19} #knitr::include_graphics(file.path(SYSTEM_JPEG_PATH, "assign_summary_EV.jpeg")) ``` Non-Mandatory Trip Flows{data-navmenu="District Summaries"} ========================================================= Description {.sidebar data-width=175} -------------------------------------------- ******** **District level flow comparison** Results of district summaries. Comparison of non-mandatory district flow summaries. Chart Column 2{.tabset} -------------------------------------------- ### TLFD by District{data-height=575} ```{r DFLOW_dis1} ``` ### TLFD by District{data-height=575} ```{r DFLOW_dis2} ```